In order to handle the problem of fault detection for industrial process

an improved multiscale principal component analysis(MSPCA)is proposed.Firstly

considering the nonstationary and random nature of data in the process industry which contains different noises inevitably

an improved wavelet threshold denoising method which combines multiple wavelet transform with a new threshold function based on the characteristics of wavelet analysis is proposed.The data collected from the industry condition are processed by means of the improved wavelet threshold denoising method.Using wavelets

the individual variable is decomposed into approximations and details at different scales.Contributions from each scale are collected in separate matrices

and a PCA model is then constructed to extract correlation at each scale.According to the simulation of propylene polymerization

and comparing the improved MSPCA with traditional MSPCA

it shows that the improved MSPCA has enhanced the accuracy of process monitoring and fault diagnosis.